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UARS

Unified Architecture Registry Standard

1. Purpose

The ZAYAZ Unified Architecture Registry Standard defines the canonical identifier systems and registries used across the ZAYAZ platform.

The standard ensures that all platform components use consistent, deterministic identifiers across:

Telemetry event tracking
API payloads
Data storage schemas
Platform orchestration
AI reasoning pipelines
Micro-engine execution
Developer tooling
Documentation

The goal is to guarantee traceability, interoperability, and governance integrity across the entire platform architecture.

This document defines the architecture and governance rules for the following registries:

OMR — Official Module Registry
Engine Registry
MICE Registry
Signal Registry (USO / CSI)
Telemetry Event Schema

Together these form the canonical identity layer of the ZAYAZ platform.

2. Architectural Overview

The ZAYAZ platform architecture is structured around five canonical registries.

ZAYAZ Identity Layer

OMR (Module Registry)
      │
      ├── Engine Registry
      │        │
      │        └── MICE Registry
      │
      ├── Signal Registry
      │        ├── USO
      │        └── CSI
      │
      └── Telemetry Event Schema

These registries define all identifiers used across the system.

All services, AI components, dashboards, APIs, and telemetry pipelines must reference identifiers from these registries.

3. Official Module Registry (OMR)

The Official Module Registry (OMR) defines the canonical modules of the ZAYAZ platform.

Modules represent governed platform domains that expose:

orchestration capabilities
service boundaries
computation domains
data ownership domains

Each module is defined by a stable OMR Module ID.

Example module identifiers:

computation-hub
input-hub
reporting
risk
net-zero
sis

Each module includes metadata describing:

domain
governance requirements
lifecycle
dependencies
signals
orchestration workflows

The canonical source of module definitions is:

config/system/omr.modules.json

The OMR provides the top-level architecture map of the platform.

All engines and micro-engines must reference their parent module through this registry.

Excellent — this final improvement organizes the whole architecture into three conceptual layers, which is how large platforms typically document their system identity and execution models.

The good news is: your platform already fits this model extremely well, so this section only clarifies what already exists without changing anything.

You can insert this near the beginning of the document (after the architectural overview).

4. ZAYAZ Platform Architecture Layers

The ZAYAZ platform is organized into three conceptual architecture layers.

These layers separate:

identity and governance
execution and computation
semantic meaning and outputs

This separation ensures that the platform remains modular, traceable, and evolvable as the system grows.

ZAYAZ Architecture Layers

┌─────────────────────────────────────┐
│ 1. Identity Layer                   │
│                                     │
│ OMR                                 │
│ Engine Registry                     │
│ MICE Registry                       │
│                                     │
│ Defines WHAT components exist       │
└─────────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────┐
│ 2. Execution Layer                  │
│                                     │
│ Engines                             │
│ Micro Engines (MICE)                │
│ Module Workflows                    │
│                                     │
│ Defines HOW computations run        │
└─────────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────┐
│ 3. Semantic Layer                   │
│                                     │
│ USO Signals                         │
│ CSI Signals                         │
│ Telemetry Events                    │
│                                     │
│ Defines WHAT results mean           │
└─────────────────────────────────────┘

These layers together form the core architecture model of the ZAYAZ platform.

4.1. Identity Layer

The Identity Layer defines the canonical identifiers used across the platform.

This layer answers the question:

“What components exist in the system?”

The identity layer includes the following registries:

Component	Registry
Modules	OMR
Engines	Engine Registry
Micro Engines	MICE Registry

These registries define the stable identifiers used across the platform.

Example identifiers:

Module:
computation-hub

Engine:
DaVE

Micro Engine:
MEID_CALC_GHG_SCOPE1

These identifiers are used across:

orchestration
telemetry
AI reasoning
governance systems

The identity layer is the foundation of the ZAYAZ architecture.

4.2. Execution Layer

The Execution Layer defines how computations are performed within the platform.

This layer answers the question:

“How does the system execute logic?”

The execution layer includes:

platform engines
micro-engine execution (MICE)
module workflows
orchestration pipelines

Example execution flow:

Module
   ↓
Engine
   ↓
Micro Engine (MICE)

Example:

Module:
computation-hub

Engine:
DaVE

Micro Engine:
MEID_CALC_GHG_SCOPE1

The execution layer performs the actual computational work of the platform.

4.3. Semantic Layer

The Semantic Layer defines the meaning of computations produced by the platform.

This layer answers the question:

“What do the results represent?”

The semantic layer includes:

Component	Purpose
USO	semantic classification
CSI	canonical outputs
Telemetry	event capture

Example semantic outputs:

USO:
DATA.COMPUTATION

CSI:
CSI_COMPUTATION_HUB

These signals allow the system to:

classify computations
enable AI reasoning
drive dashboards
support telemetry analytics

The semantic layer ensures that platform outputs are interpretable and traceable.

4.4. Why This Architecture Matters

Separating identity, execution, and semantics provides several advantages.

Platform Stability

Identifiers remain stable even when computation logic changes.

AI Explainability

The system can explain:

What module handled the request
Which engine performed computation
Which micro-engine executed logic
What signals were produced

Auditability

The architecture enables deterministic traceability from telemetry events back to the originating module.

Scalable Evolution

Each layer can evolve independently:

Layer	Evolution
Identity	new modules, engines
Execution	new algorithms
Semantics	new signals

This allows the platform to grow without breaking existing workflows.

4.5. Layer Interaction Example

Example execution trace across the three layers:

Identity Layer
Module: computation-hub
Engine: DaVE
Micro Engine: MEID_CALC_GHG_SCOPE1

Execution Layer
Scope 1 emissions computation

Semantic Layer
USO: DATA.COMPUTATION
CSI: CSI_COMPUTATION_HUB

Telemetry Event
compute_completed

This structure ensures that every platform output can be traced across all three architecture layers.

4.6. ZAYAZ Computation Flow Diagram

The following diagram illustrates how data and computation move through the ZAYAZ platform from user input to telemetry and AI reasoning.

What This Diagram Shows

The diagram highlights six critical architectural steps:

1️⃣ Input User actions, API calls, or data imports enter the platform.

2️⃣ Identity Resolution The system determines:

which module
which engine
which micro-engine

should handle the request.

3️⃣ Execution The computation is executed through:

Module → Engine → Micro Engine (MICE)

4️⃣ Semantic Interpretation Signals classify the result:

USO → semantic meaning
CSI → canonical output identifier

5️⃣ Telemetry Capture Every operation produces a telemetry event.

6️⃣ AI / Analytics / Governance Downstream systems consume telemetry and signals for:

dashboards
AI reasoning
compliance audits
observability

Why This Diagram Matters

This computation flow demonstrates how the ZAYAZ architecture ensures:

deterministic computation pipelines
traceable AI reasoning
governance-grade observability
scalable micro-engine orchestration

Every platform output can therefore be traced across:

Module → Engine → Micro Engine → Signal → Telemetry

which forms the core traceability chain of the ZAYAZ platform.

4.7. ZAYAZ Platform Architecture

5. Engine Registry

The Engine Registry defines all computational engines used within the platform.

Engines represent major computational services responsible for domain-level logic.

Examples:

DICE
DaVE
SEM
BUME
FOGE
COSE
RIF

Each engine entry includes:

engine ID
engine code
lifecycle metadata
documentation references
module ownership (OMR reference)

Example:

engine_id: AAE
engine_kind: micro_engine
meid: MEID_ASRE_AAE
omr_module_ids:
  - computation-hub

The canonical registry is generated from documentation frontmatter:

config/system/registry.json

This registry acts as the canonical inventory of platform engines.

6. MICE Registry

(Micro-Engine Identity System)

The MICE layer defines the micro-engine execution architecture.

A Micro Engine is a small, composable computation component executed by the MICE execution layer.

Examples include:

emission factor computations
Bayesian updates
scenario simulations
anomaly detection
compliance mapping

Each micro-engine is identified by a MEID (Micro Engine Identifier).

Example identifiers:

MEID_ASRE_AAE
MEID_CALC_GHG_SCOPE1
MEID_CALC_WATER_INT
MEID_SCEN_GHG_ALIGN

MEIDs uniquely identify a computation capability across:

orchestration layers
telemetry
governance
audit trails

Micro-engines belong to one or more modules via:

omr_module_ids

The MICE registry is generated from:

registry.json

and consumed by platform services that perform micro-engine routing and orchestration.

7. Signal Registry

Signals represent the canonical semantic outputs of platform computation.

Signals ensure that AI reasoning, telemetry, and downstream systems share a common semantic layer.

The signal system consists of two primary components.

7.1. USO (Universal Signal Ontology)

USO defines semantic signal classes used across platform modules.

Examples:

DATA.COMPUTATION
DATA.VALIDATION
MODEL.SIMULATION
MODEL.UNCERTAINTY

USO tags describe what type of information is being produced or processed.

These tags are used for:

system classification
AI reasoning context
signal routing
governance tagging

7.2. CSI (Canonical Signal Identifiers)

CSI identifiers represent concrete outputs produced by engines or modules.

Example:

CSI_COMPUTATION_HUB

CSI identifiers ensure that signals are traceable across platform pipelines.

Each module defines the CSI signals it produces.

8. Telemetry Event Schema

The ZAYAZ telemetry architecture captures events across the platform.

Every telemetry event must reference identifiers from the canonical registries.

Example telemetry event:

track(
  event_type="validation_error",
  module="COMP_HUB",
  engine="DaVE",
  micro_engine="MEID_CALC_GHG_SCOPE1",
  client_id="eco123456789",
  user_id="usr-eco123456789-0000001",
  field_id="GHG_Scope3",
  error_message="Missing input"
)

Telemetry events must reference:

Field	Registry Source
module	OMR
engine	Engine Registry
micro_engine	MICE Registry
signal	Signal Registry

This ensures telemetry events are fully traceable to architecture components.

9. Naming Conventions

The ZAYAZ platform enforces strict naming rules to maintain architectural clarity.

Module IDs

Format:

kebab-case

Examples:

computation-hub
input-hub
reporting

Module Codes

Format:

UPPER_SNAKE_CASE

Examples:

COMP_HUB
INPUT_HUB

Engine IDs

Format:

UPPERCASE short identifiers

Examples:

DICE
SEM
BUME
COSE

Intentional mixed-case names are allowed where historically defined.

Example:

DaVE

Micro Engine IDs (MEID)

Format:

MEID_<DOMAIN>_<NAME>

Examples:

MEID_ASRE_AAE
MEID_CALC_GHG_SCOPE1
MEID_SCEN_GHG_ALIGN

10. Registry Generation

All ZAYAZ registries are generated automatically from documentation and configuration sources.

Key registries include:

config/system/omr.modules.json
config/system/registry.json
config/system/engine-aliases.json

These registries are generated by build scripts and consumed by:

documentation tooling
architecture visualizations
AI orchestration layers
telemetry pipelines
developer tooling

Manual editing of registry outputs is prohibited.

11. Governance Rules

The following rules apply to the ZAYAZ identity layer.

No ad-hoc identifiers

All identifiers must originate from a canonical registry.

Registry updates require governance approval

Changes to:

module identifiers
engine identifiers
MEIDs
signal identifiers

must follow the platform governance process.

Backwards compatibility

Identifiers must remain stable once introduced.

Deprecation procedures must be used for removal.

12. Why This Standard Matters

This architecture ensures that every component in the ZAYAZ platform can be traced through a consistent identity layer.

This enables:

deterministic AI reasoning
audit-grade traceability
scalable telemetry pipelines
reliable micro-engine orchestration
consistent documentation
enterprise-grade governance

Without a unified identity system, platform telemetry, orchestration, and AI reasoning would fragment across inconsistent identifiers.

13. ZAYAZ Architecture Identity Map

The ZAYAZ platform uses a layered identity architecture to ensure that every computational capability, signal, and telemetry event can be traced to a governed platform component.

The identity system connects modules, engines, micro-engines, signals, and telemetry events into a unified architecture.

Identity Layer Overview

                    ZAYAZ PLATFORM

              ┌────────────────────────┐
              │  Official Module       │
              │  Registry (OMR)        │
              │                        │
              │ computation-hub        │
              │ reporting              │
              │ risk                   │
              │ input-hub              │
              └──────────┬─────────────┘
                         │
                         │ module ownership
                         │
              ┌──────────▼──────────┐
              │ Engine Registry     │
              │                     │
              │ DICE                │
              │ DaVE                │
              │ SEM                 │
              │ BUME                │
              │ COSE                │
              └──────────┬──────────┘
                         │
                         │ execution
                         │
              ┌──────────▼──────────┐
              │ MICE Registry       │
              │ (Micro Engines)     │
              │                     │
              │ MEID_CALC_GHG_SCOPE1│
              │ MEID_CALC_WATER_INT │
              │ MEID_SCEN_GHG_ALIGN │
              │ MEID_ASRE_AAE       │
              └──────────┬──────────┘
                         │
                         │ signal production
                         │
              ┌──────────▼──────────┐
              │ Signal Registry     │
              │                     │
              │ USO                 │
              │ CSI                 │
              │                     │
              │ DATA.COMPUTATION    │
              │ MODEL.SIMULATION    │
              │ CSI_COMPUTATION_HUB │
              └──────────┬──────────┘
                         │
                         │ event capture
                         │
              ┌──────────▼──────────┐
              │ Telemetry Schema    │
              │                     │
              │ module              │
              │ engine              │
              │ micro_engine        │
              │ signal              │
              └─────────────────────┘

This identity architecture ensures that every computation executed within the platform can be traced through the entire architecture stack.

For example:

Telemetry Event
     ↓
Micro Engine (MEID)
     ↓
Engine
     ↓
Module (OMR)

This guarantees:

deterministic AI reasoning
auditable computation pipelines
traceable data lineage
governance-grade system observability

14. End-to-End Traceability Example

The ZAYAZ identity architecture enables complete traceability from user input to AI output.

The following example illustrates a full platform execution path.

Step 1 — Module Context

A user submits emissions data within the Computation Hub module.

module_id: computation-hub
module_code: COMP_HUB

This module governs computation workflows related to ESG metrics and modeling.

Step 2 — Engine Execution

The computation is handled by the Diagnostic Validation Engine.

engine_id: DaVE

The engine performs validation checks on submitted emissions data.

Step 3 — Micro Engine Invocation

The engine invokes a specific micro-engine within the MICE layer.

micro_engine: MEID_CALC_GHG_SCOPE1

This micro-engine calculates Scope 1 greenhouse gas emissions based on submitted operational data.

Step 4 — Signal Generation

The micro-engine produces a signal describing the computation result.

Example signals:

USO tag:
DATA.COMPUTATION

CSI output:
CSI_COMPUTATION_HUB

These signals provide semantic meaning to the computation.

Step 5 — Telemetry Event Capture

The platform records a telemetry event describing the execution.

Example:

track(
  event_type="compute_completed",
  module="COMP_HUB",
  engine="DaVE",
  micro_engine="MEID_CALC_GHG_SCOPE1",
  signal="CSI_COMPUTATION_HUB",
  client_id="eco123456789",
  timestamp_utc="2025-04-26T10:15:32Z"
)

This event enables the system to trace:

which module handled the request
which engine executed logic
which micro-engine performed computation
which signals were produced

Step 6 — Governance and Audit

Because each component references a canonical registry identifier, the system can reconstruct the entire execution chain:

Telemetry Event
      ↓
Micro Engine
      ↓
Engine
      ↓
Module
      ↓
Signals

This enables:

full auditability of AI reasoning
replayable computation pipelines
explainable model outputs
enterprise-grade governance

Why This Matters

This architecture ensures that every output generated by the ZAYAZ platform has a deterministic identity trail.

This identity trail is critical for:

regulatory compliance
enterprise trust
AI explainability
debugging complex workflows
scaling the platform safely

The identity architecture is therefore a core foundation of the ZAYAZ platform design.

15. Registry Evolution & Versioning Strategy

The ZAYAZ platform is designed to evolve continuously as new modules, engines, signals, and micro-engines are introduced.

To maintain system stability and traceability, all registries follow a strict versioning and evolution strategy.

This ensures that:

telemetry pipelines remain stable
AI reasoning remains reproducible
historical computations remain auditable
platform upgrades do not break existing integrations

15.1. Registry Stability Principles

All ZAYAZ registries follow four stability principles.

Identifier Stability

Once an identifier is introduced, it must never change.

Identifiers include:

module IDs (OMR)
engine IDs
micro-engine identifiers (MEID)
signal identifiers (CSI / USO)

Example:

MEID_CALC_GHG_SCOPE1

This identifier must remain stable across all future platform versions.

Changing identifiers would break:

telemetry queries
audit logs
AI training datasets
historical dashboards

Append-Only Evolution

Registries evolve using an append-only model.

New identifiers are added, but existing identifiers are never modified or reused.

Example evolution:

MEID_CALC_GHG_SCOPE1
MEID_CALC_GHG_SCOPE2
MEID_CALC_GHG_SCOPE3

Older identifiers remain valid even if they are no longer actively used.

Explicit Deprecation

When a module, engine, or signal becomes obsolete, it is deprecated rather than removed.

Deprecation metadata is recorded in the registry.

Example:

lifecycle:
  status: deprecated
  deprecated_in: v2.4

Deprecated identifiers remain available for:

telemetry replay
historical audit
legacy workflows

Deterministic Traceability

Every registry change must preserve the ability to reconstruct historical system behavior.

This ensures that the platform can answer questions such as:

Which engine produced this result?
Which micro-engine performed the computation?
Which signals were generated?
Which module owned the workflow?

This capability is critical for:

AI explainability
regulatory audits
incident investigation
debugging complex workflows

15.2. Module Evolution

Modules evolve slowly because they represent major architectural domains.

Typical module evolution includes:

adding new workflows
adding new signals
adding new engines
expanding governance metadata

Example lifecycle:

module_id: computation-hub
status: active
introduced_in: v0.3

Modules are rarely removed. When necessary, they are marked as deprecated.

15.3. Engine Evolution

Engines represent domain computation services.

Engines may evolve by:

improving algorithms
introducing new orchestration logic
invoking new micro-engines

Engine identifiers remain stable even when internal logic changes.

Example:

engine_id: DaVE
version: 2.1

The identifier remains constant while the implementation evolves.

15.4. Micro-Engine Evolution (MICE)

Micro-engines evolve more frequently because they represent small, composable computations.

A new MEID is created when:

a computation changes semantics
a new algorithm replaces an existing one
output structure changes

Example:

MEID_CALC_GHG_SCOPE1
MEID_CALC_GHG_SCOPE1_V2

Both versions may coexist to support:

legacy telemetry
reproducible modeling
audit replay

15.5. Signal Evolution

Signals evolve as the platform introduces new analytical capabilities.

Signal evolution follows the same append-only strategy.

Example:

DATA.COMPUTATION
DATA.VALIDATION
MODEL.SIMULATION
MODEL.UNCERTAINTY

New signal types may be introduced without altering existing ones.

CSI identifiers remain stable once published.

15.6. Telemetry Compatibility

Telemetry pipelines depend on stable identifiers.

Because registry identifiers never change, telemetry queries remain valid across platform upgrades.

Example telemetry query:

SELECT *
FROM telemetry_events
WHERE module = "COMP_HUB"

This query will remain valid even if:

new engines are introduced
new micro-engines are deployed
new signals are added

15.7. Registry Governance Workflow

Registry changes follow the platform governance process.

Typical workflow:

Proposal
   ↓
Architecture review
   ↓
Registry update
   ↓
Platform release

Changes requiring review include:

new modules
new engines
new MEIDs
new signal identifiers

This ensures the identity architecture remains consistent as the platform scales.

15.8. Why Versioning Matters

Without strict registry evolution rules, large AI platforms often suffer from:

telemetry fragmentation
inconsistent identifiers
untraceable model outputs
broken dashboards
unreliable audit trails

The ZAYAZ registry architecture prevents these issues by ensuring that identity remains stable even as computation evolves.

This strategy allows the platform to scale safely while maintaining:

architectural clarity
governance integrity
AI explainability
enterprise-grade observability

16. Platform Identity Reference Table

The ZAYAZ platform uses several distinct identifier types to ensure that modules, engines, micro-engines, signals, and telemetry events can be uniquely and consistently referenced across the system.

The following table provides a quick reference for all identity systems used within the platform architecture.

Identity Type	Example	Format	Registry Source	Used In
Module ID (OMR)	computation-hub	kebab-case	omr.modules.json	Architecture, orchestration
Module Code	COMP_HUB	UPPER_SNAKE_CASE	omr.modules.json	Telemetry, APIs
Engine ID	DaVE	Uppercase short name	registry.json	Engine orchestration
Engine Code	DICE, SEM	Uppercase	registry.json	Internal references
Micro Engine ID (MEID)	MEID_CALC_GHG_SCOPE1	MEID_*	registry.json	MICE execution
ZAR Code	MIE12	short internal code	MICE registry	Internal referencing
USO Signal	DATA.COMPUTATION	dot namespace	Signal registry	Semantic classification
CSI Signal	CSI_COMPUTATION_HUB	uppercase constant	Signal registry	Output signals
Telemetry Event Type	validation_error	snake_case	telemetry schema	Event tracking
Client ID	eco123456789	system-generated	client registry	telemetry
User ID	usr-eco123456789-0000001	structured ID	identity service	telemetry

16.1. Registry Source Locations

The canonical registry files for the ZAYAZ platform are stored within the platform configuration layer.

Primary registry locations:

config/system/omr.modules.json
config/system/registry.json
config/system/engine-aliases.json

These registries are generated automatically and consumed by:

documentation tooling
architecture visualization systems
platform orchestration layers
telemetry pipelines
developer tooling

Manual modification of registry outputs is not permitted.

16.2. Identity Layer Summary

The ZAYAZ platform identity architecture ensures that every computation can be traced across multiple layers.

Module (OMR)
   ↓
Engine
   ↓
Micro Engine (MEID)
   ↓
Signal (USO / CSI)
   ↓
Telemetry Event

This identity chain guarantees that every platform output can be traced to:

the governing module
the engine responsible for computation
the micro-engine performing the operation
the signal produced
the telemetry event recorded

This traceability forms the foundation for:

AI explainability
auditability
operational observability
enterprise compliance

17. Registry Validation Rules

To maintain the integrity of the ZAYAZ identity architecture, all platform registries are automatically validated during the build process.

Registry validation ensures that identifiers remain:

unique
structurally valid
correctly referenced
consistent across modules, engines, signals, and telemetry

These validation rules are enforced through platform scripts and continuous integration checks.

17.1. Validation Objectives

Registry validation ensures that:

identifiers follow the required naming conventions
modules reference valid engines
engines reference valid modules
micro-engines reference valid modules
signals follow the canonical ontology
telemetry identifiers remain consistent

This prevents architecture drift and guarantees that the identity layer remains reliable as the platform evolves.

17.2. Module Registry Validation

The OMR registry must satisfy the following conditions.

Module IDs must be unique

Example valid identifiers:

computation-hub
input-hub
reporting
risk

Duplicate module identifiers are rejected during validation.

Module codes must be unique

Example:

COMP_HUB
INPUT_HUB

Codes are used in telemetry and must therefore remain globally unique.

Module dependencies must reference valid modules

Example:

dependencies:
  modules:
    - sis
    - input-hub

Validation rules ensure that referenced modules exist in the OMR.

17.3. Engine Registry Validation

The Engine Registry must satisfy the following rules.

Engine IDs must be unique

Example:

DICE
SEM
DaVE
COSE

Engines must reference valid modules

Example:

omr_module_ids:
  - computation-hub

Validation ensures that each referenced module exists in the OMR registry.

Engine codes must be unique

Engine codes are used in telemetry and orchestration pipelines.

Duplicate codes are rejected.

Note: Engine codes are the same as their frontmatter id in this manual and their code can be seen as the green text over their document's heading.

17.4. Micro Engine Validation (MICE)

Micro-engine identifiers must follow strict rules.

MEID format validation

Valid format:

MEID_<DOMAIN>_<NAME>

Example:

MEID_CALC_GHG_SCOPE1
MEID_CALC_WATER_INT
MEID_SCEN_GHG_ALIGN

Invalid identifiers are rejected during registry generation.

Micro-engines must belong to at least one module

Example:

omr_module_ids:
  - computation-hub

This ensures every micro-engine has a defined architectural domain.

17.5. Signal Registry Validation

Signals must conform to the signal ontology.

USO signals

Format:

DOMAIN.SIGNAL_TYPE

Example:

DATA.COMPUTATION
MODEL.SIMULATION
DATA.VALIDATION

CSI identifiers

Format:

CSI_<MODULE>

Example:

CSI_COMPUTATION_HUB

Signals must be unique within the signal registry.

17.6. Telemetry Schema Validation

Telemetry events must reference valid identifiers.

Example event:

track(
  event_type="validation_error",
  module="COMP_HUB",
  engine="DaVE",
  micro_engine="MEID_CALC_GHG_SCOPE1"
)

Validation ensures that:

module exists in OMR
engine exists in Engine Registry
micro_engine exists in the MICE registry

Events referencing invalid identifiers are rejected during build validation.

17.7. Cross-Registry Validation

The build process also performs cross-registry checks.

Examples include:

Engine-to-module mapping

Every engine must reference a valid module.

Micro-engine ownership validation

Every MEID must reference a valid module.

Module engine inventory validation

Module definitions referencing engines must match entries in the engine registry.

Dependency resolution

Dependencies listed in modules must reference valid modules or engines.

Unresolved dependencies are flagged during registry generation.

17.8. CI Enforcement

Registry validation is executed automatically during:

documentation builds
registry generation scripts
continuous integration pipelines

Example workflow:

Generate registries
      ↓
Run validation checks
      ↓
Build documentation
      ↓
Deploy platform

If validation fails, the build process is halted.

This ensures that inconsistent identifiers cannot enter the platform.

17.9. Why Validation Matters

Without automated registry validation, large systems often suffer from:

identifier drift
broken telemetry pipelines
inconsistent documentation
hard-to-debug orchestration failures

The ZAYAZ validation layer prevents these issues by enforcing strict identity discipline across the platform architecture.

Final Note

The ZAYAZ platform identity system is intentionally designed to be:

deterministic — identifiers never change
composable — modules, engines, and micro-engines combine cleanly
traceable — every computation can be reconstructed
governed — changes follow formal architecture processes

It is protected by three governance layers:

1️⃣ Canonical registries

2️⃣ Append-only evolution rules

3️⃣ Automated validation checks

Together these ensure that the architecture remains stable, traceable, and scalable as the platform grows.

Maintaining strict adherence to this identity architecture ensures that the ZAYAZ platform can scale safely while preserving clarity, auditability, and engineering discipline.

GitHub Repo Request for Change (RFC)

1. Purpose​

2. Architectural Overview​

3. Official Module Registry (OMR)​

4. ZAYAZ Platform Architecture Layers​

4.1. Identity Layer​

4.2. Execution Layer​

4.3. Semantic Layer​

4.4. Why This Architecture Matters​

4.5. Layer Interaction Example​

4.6. ZAYAZ Computation Flow Diagram​

4.7. ZAYAZ Platform Architecture​

5. Engine Registry​

6. MICE Registry​

7. Signal Registry​

7.1. USO (Universal Signal Ontology)​

7.2. CSI (Canonical Signal Identifiers)​

8. Telemetry Event Schema​

9. Naming Conventions​

10. Registry Generation​

11. Governance Rules​

12. Why This Standard Matters​

13. ZAYAZ Architecture Identity Map​

14. End-to-End Traceability Example​

Step 1 — Module Context​

Step 2 — Engine Execution​

Step 3 — Micro Engine Invocation​

Step 4 — Signal Generation​

Step 5 — Telemetry Event Capture​

Step 6 — Governance and Audit​

15. Registry Evolution & Versioning Strategy​

15.1. Registry Stability Principles​

15.2. Module Evolution​

15.3. Engine Evolution​

15.4. Micro-Engine Evolution (MICE)​

15.5. Signal Evolution​

15.6. Telemetry Compatibility​

15.7. Registry Governance Workflow​

15.8. Why Versioning Matters​

16. Platform Identity Reference Table​

16.1. Registry Source Locations​

16.2. Identity Layer Summary​

17. Registry Validation Rules​

17.1. Validation Objectives​

17.2. Module Registry Validation​

17.3. Engine Registry Validation​

17.4. Micro Engine Validation (MICE)​

17.5. Signal Registry Validation​

17.6. Telemetry Schema Validation​

17.7. Cross-Registry Validation​

17.8. CI Enforcement​

17.9. Why Validation Matters​

Final Note​

1. Purpose

2. Architectural Overview

3. Official Module Registry (OMR)

4. ZAYAZ Platform Architecture Layers

4.1. Identity Layer

4.2. Execution Layer

4.3. Semantic Layer

4.4. Why This Architecture Matters

4.5. Layer Interaction Example

4.6. ZAYAZ Computation Flow Diagram

4.7. ZAYAZ Platform Architecture

5. Engine Registry

6. MICE Registry

7. Signal Registry

7.1. USO (Universal Signal Ontology)

7.2. CSI (Canonical Signal Identifiers)

8. Telemetry Event Schema

9. Naming Conventions

10. Registry Generation

11. Governance Rules

12. Why This Standard Matters

13. ZAYAZ Architecture Identity Map

14. End-to-End Traceability Example

Step 1 — Module Context

Step 2 — Engine Execution

Step 3 — Micro Engine Invocation

Step 4 — Signal Generation

Step 5 — Telemetry Event Capture

Step 6 — Governance and Audit

15. Registry Evolution & Versioning Strategy

15.1. Registry Stability Principles

15.2. Module Evolution

15.3. Engine Evolution

15.4. Micro-Engine Evolution (MICE)

15.5. Signal Evolution

15.6. Telemetry Compatibility

15.7. Registry Governance Workflow

15.8. Why Versioning Matters

16. Platform Identity Reference Table

16.1. Registry Source Locations

16.2. Identity Layer Summary

17. Registry Validation Rules

17.1. Validation Objectives

17.2. Module Registry Validation

17.3. Engine Registry Validation

17.4. Micro Engine Validation (MICE)

17.5. Signal Registry Validation

17.6. Telemetry Schema Validation

17.7. Cross-Registry Validation

17.8. CI Enforcement

17.9. Why Validation Matters

Final Note